Topic modelling

Case of Parliamentary interpellation

Introduction

Topic modeling has emerged as a powerful computational technique for uncovering latent themes and patterns within large textual corpora, offering valuable insights into the underlying structures of complex discourse domains. Within the context of the Polish parliament’s interpellation process, where members engage in diverse debates and inquiries on matters of national significance, topic modeling presents a promising approach to discerning the underlying thematic content and dynamics.

Purpose of the project

By applying topic modeling algorithms to the corpus of interpellation texts, this study seeks to identify the key issues, policy domains, and thematic clusters that dominate parliamentary discourse. Through this analysis, I aim to shed light on the salient topics, evolving priorities, and ideological fault lines within the Polish political landscape, thereby contributing to a deeper understanding of legislative agendas, public policy, and democratic governance in Poland.

Main assumptions (unexamined beliefs about the case)

1. There are specific topics emerge as dominant themes across the interpellation corpus, indicating their prevalence and importance within the legislative agenda.
2. Importance of topics may vary across different parliamentary sessions, political factions, or policy domains, reflecting shifts in public attention, government priorities, or societal concerns over time.

Data

Description of the data set

Data comes from The Polish Parliamentary Corpus / Korpus Dyskursu Parlamentarnego repository. That repository contains both Sejm and Senat proceedings, interpellation etc. from 1919 to 2023. The analysis is limited to 3 parliament terms (1997-2001, 2001-2005 and 2005-200) and only to data from Member of Parliament interpellations. Based on the files stored in that repository the dataset for the analysis (both the content and available metadata) has been created and preprocessed before the analysis via external scripts link to reposiotry of this project). To stem data KRNNT tool was used. Preprocessed files are available on Google Drive.

Preparation of data for modeling

There will be three main steps:

• retrieve date of interpellation since it was available in metadata
• make sure author is ok - it is needed for proper network analysis
• clean stemmed data

Load data

First step - load data from preprocessed files

pi_files <- list.files("data", pattern = "^(pi_)")
all_pi <- do.call(rbind, 
                  lapply(X = pi_files, FUN = function(i){
                    mydb <- dbConnect(SQLite(), file.path("data", i))
                    this_cont <- dbGetQuery(conn = mydb, 
                                            statement = "select metadata.*, ipcontent.content from ipcontent left join metadata on metadata.id = ipcontent.id")
                    dbDisconnect(mydb)
                    this_cont$period <- gsub("pi_", replacement = "", x = gsub(pattern = ".sqlite", replacement = "", x = i))
                    this_cont
                  }))
# add information about length of each document
all_pi <- all_pi %>% 
  mutate(len = nchar(CONTENT))

# create dataset with start and end of each period
period_bands <- data.frame(period = c("1997-2001", "2001-2005", "2005-2007"), 
                           start_date = as.Date(c("1997-10-20", "2001-10-19", "2005-10-19")),
                           end_date = as.Date(c("2001-10-18", "2005-10-18", "2007-09-07")))

Create date of interpellation

Second step - create date of creation of each interpellation

# assumption - each document ends in date passed in that format: <day> <month as a word> <year>
last_part_len <- 24
vec_to_extract_dates <- all_pi$CONTENT %>% substr(start = nchar(.) - last_part_len, nchar(.))
# there are some cases that will be overwritten and not treated with general approach (59 out of 28074 [0.2%]):
#   because the last part of document did not include date (e.g. it consists of footnotes of some sort)
#   because there were mistakes (e.g. year 3000)
list_exceptions <- fromJSON(txt = "dicts/pi_dates.json")
vec_to_extract_dates[match(x = names(list_exceptions), table = all_pi$DOC)] <- unlist(list_exceptions)

# remove last part of date
vec_to_extract_dates <- gsub(pattern = "(r|roku)\\s*\\.*\\,*$", replacement = "", x = vec_to_extract_dates)
vec_to_extract_dates <- trimws(vec_to_extract_dates)
# extract year
years <- as.numeric(substr(vec_to_extract_dates, regexpr(pattern = "\\d+\\.*$", text = vec_to_extract_dates), nchar(vec_to_extract_dates)))
# check if everything is ok
if (any(is.na(years))) {
  idx <- which(is.na(years))
  message(length(idx))
  vec_to_extract_dates[idx[1:min(10, length(idx))]]
}

if (!all(years %in% c(1997:2007))) {
  idx <- which(!years %in% c(1997:2007))
  message(length(idx))
  vec_to_extract_dates[idx[1:min(10, length(idx))]]
}

# remove year to ease the process
vec_to_extract_dates <- trimws(substr(vec_to_extract_dates, 1, nchar(vec_to_extract_dates) - 4))
# extract month
months_vec <- rep(0, length = length(vec_to_extract_dates))
months_dict <- list("1" = "stycznia",
                    "2" = c("lutego", "luty"),
                    "3" = "marca",
                    "4" = c("kwietnia", "kwietnie", "kwitnia", "kwietna"),
                    "5" = "maja",
                    "6" = "czerwca",
                    "7" = "lipca", 
                    "8" = "sierpnia",
                    "9" = c("września", "wrzesień"),
                    "10" = "października",
                    "11" = c("listopada", "listopad"),
                    "12" = "grudnia")

for (m in 1:length(months_dict)) {
  vals <- regexpr(pattern = sprintf("(%s)", paste0(months_dict[[m]], collapse = "|")), text = vec_to_extract_dates)
  idx <- which(vals > 0)
  months_vec[idx] <- as.numeric(names(months_dict)[m])
  vec_to_extract_dates[idx] <- substr(vec_to_extract_dates[idx], 1, vals[idx] -1)
}

# check if everything is ok
if (!all(months_vec %in% c(1:12))) {
  idx <- which(!months_vec %in% c(1:12))
  message(length(idx))
  vec_to_extract_dates[idx[1:min(10, length(idx))]]
}

vec_to_extract_dates <- trimws(vec_to_extract_dates)
# extract day
days_vec <- as.numeric(substr(vec_to_extract_dates, regexpr(pattern = "\\d+$", text = vec_to_extract_dates), nchar(vec_to_extract_dates)))

# check if everything is ok
if (!all(days_vec %in% c(1:31))) {
  idx <- which(!days_vec %in% c(1:31))
  message(length(idx))
  vec_to_extract_dates[idx[1:min(10, length(idx))]]
}

# add to dataset
all_pi$date <- as.Date(sprintf("%s-%02d-%02d", years, months_vec, days_vec))

Check if all is ok - each date should be between start and end of each period

all_pi %>% 
  left_join(y = period_bands, by = "period") %>% 
  mutate(days_after_start = as.numeric(date - start_date)) %>% 
  filter(days_after_start < 0) %>% 
  kable() %>%
    kable_styling()

ID	AUTHOR	DOC	CONTENT	period	len	date	start_date	end_date	days_after_start
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
–:	:——	:—	:——-	:——	—:	:—-	:———-	:——–	—————-:

But sometimes interpellation is written in a period t is delivered to Sejm in period t-1.

all_pi %>% 
  left_join(y = period_bands, by = "period") %>% 
  mutate(days_before_end = as.numeric(end_date - date)) %>% 
  filter(days_before_end < 0) %>% 
  summarise(n(), min(days_before_end), median(days_before_end), max(days_before_end)) %>% 
  kable() %>%
    kable_styling()

n()	min(days_before_end)	median(days_before_end)	max(days_before_end)
31	-172	-11	-1

Clean up authors

Third step - cleaning up data regarding author of interpellation

all_pi %>% group_by(period) %>% 
  summarise(n_authors = length(unique(AUTHOR))) %>% 
  kable() %>% 
  kable_styling()

period	n_authors
1997-2001	559
2001-2005	687
2005-2007	686

This is strange, those supposed to be parliamentary interpellations. In Polish Sejm there is 460 members, but data for each term suggest there are many more of them.

Couple things to consider:

• member of parliament was sometimes described with first and second name (‘Adam Bielan’ and ‘Adam Jerzy Bielan’ is the same person)
• couple of members cosign one interpellation (e.g. ‘Adam Bielan i Zbigniew Ziobro’)
• in course of term member’s list might change due to different things (death, resigning etc.)

Load the member list. This is a list of member for each term at the end of the term.

# for further analysis
posl_json <- fromJSON(txt = "dicts/poslowie.json")
posl_df <- do.call(rbind, 
                   lapply(names(posl_json),
                          FUN = function(i){
                            posl_json_period <- posl_json[[i]]
                            posl_df <- do.call(rbind,
                                               lapply(1:length(posl_json_period), 
                                                      FUN = function(j){
                                                        nn <- names(posl_json_period)[j]
                                                        ps <- unlist(posl_json_period[[nn]])
                                                        data.frame(AUTHOR = ps, ugr = names(posl_json_period)[j])
                                                      }))
                            row.names(posl_df) <- NULL
                            posl_df$period <- i
                            posl_df
                          }))

Is everything is ok with that data?

posl_df %>% 
  group_by(period) %>% 
  summarise(n = n(), uq = length(unique(AUTHOR)), any_duplicates = n > uq, members_dup = paste0(AUTHOR[duplicated(AUTHOR)], collapse = ", ")) %>% 
  kable() %>% 
  kable_styling()

period	n	uq	any_duplicates	members_dup
1997-2001	460	459	TRUE	Maciej Jankowski
2001-2005	460	459	TRUE	Ewa Janik
2005-2007	460	460	FALSE

There are duplicates in two periods. Let see how many interpellations are for those members.

all_pi %>% filter(regexpr(text = AUTHOR, pattern = "(Ewa Janik)|(Maciej Jankowski)") > 0) %>% 
  group_by(period, AUTHOR) %>% summarise(n()) %>% 
  kable() %>% 
  kable_styling()

period	AUTHOR	n()
1997-2001	Ewa Janik	29
1997-2001	Ewa Janik i Seweryn Kaczmarek	2
1997-2001	Ewa Janik i Władysław Szkop	2
1997-2001	Maciej Jankowski	2
2005-2007	Ewa Janik	3

Apparently there are only 2 (out of 28k) observations are for the periods where there are mulitple names on member list. For simplicity I’ll remove one of those members.

posl_df <- posl_df %>% filter(!(AUTHOR == "Maciej Jankowski" & ugr == "Posłowie niezrzeszeni" & period == "1997-2001"))
posl_df <- posl_df %>% filter(!(AUTHOR == "Ewa Janik" & period == "2001-2005"))
posl_df <- rbind(posl_df, data.frame(AUTHOR = "Ewa Janik", period = "2001-2005", ugr = "Klub Parlamentarny Sojuszu Lewicy Demokratycznej"))

Check how many interpellations are not accounted for author’s party

all_pi %>% 
  left_join(posl_df, by = c("AUTHOR", "period")) %>% 
  group_by(period) %>% 
  summarise(n_mem = n_distinct(AUTHOR), count = n(), without_party = sum(is.na(ugr)) / count, 
            n_mem_without_party = length(unique(AUTHOR[is.na(ugr)])), without_part_count = n_mem_without_party / n_mem) %>% 
  kable() %>% 
  kable_styling()

period	n_mem	count	without_party	n_mem_without_party	without_part_count
1997-2001	559	7444	0.1575766	276	0.4937388
2001-2005	687	10906	0.1444159	322	0.4687045
2005-2007	686	9724	0.2317976	317	0.4620991

• good news: between 1 in 7 and 1 in 5 interpollation is not accounted for auther (not great, not terrible) (column without_party)
• bad news: half of authors are not recognized (column without_part_count)

Let see which authors do not rest in posel list

all_pi %>%  
  left_join(posl_df, by = c("AUTHOR", "period")) %>% 
  group_by(AUTHOR, ugr) %>% 
  summarise(k = n()) %>% 
  filter(is.na(ugr)) %>% 
  head() %>% 
  kable() %>% 
  kable_styling()

AUTHOR	ugr	k
Adam Bielan	NA	6
Adam Bielan i Zbigniew Ziobro	NA	4
Adam Jerzy Bielan	NA	5
Adam Markiewicz i Andrzej Otręba	NA	1
Adam Ołdakowski i Maria Zbyrowska	NA	1
Adam Ołdakowski i Józef Stępkowski	NA	31

As stated before

• member was sometimes described with first and second name (‘Adam Bielan’ vs ‘Adam Jerzy Bielan’)
• couple of members cosign one interpellation (e.g. ‘Adam Bielan i Zbigniew Ziobro’)

# create dict author + period
un_authors <- all_pi %>% select(AUTHOR, period) %>% unique()
un_authors <- un_authors %>% left_join(y = posl_df, by = c("AUTHOR", "period"))

To clean up that mess I’ll introduce:

• one manual change
• assume that first member that signs the interpellation is its author
• if posel uses second name shorten it to first name and surname

# some manual changes
un_authors$AUTHOR[un_authors$AUTHOR == "łukasz Zbonikowski"] <- "Łukasz Zbonikowski"
# assumption - if multiple poeple sign a interplataion - assign it to the first on the list (most proboalby person responisbile for phrasing)
#   most likely it will be Jan Kowalski i Zbigniew Nowak (so they are separated by small i)
# if posel uses second name - shorten it to first name and surname
un_authors$stripped <- lapply(X = un_authors$AUTHOR, 
                              FUN = function(i){
                                if (i == "na") return(i)
                                gg <- strsplit(i, split = "(\\s|Senator|Poseł|Posel|Poslowie|Z należytym szacunkiem)")[[1]]
                                gg <- trimws(gg)
                                gg <- gg[nchar(gg) > 0]
                                ggs <- regexpr(pattern = "^[[:upper:]]", text = gg) > 0
                                first_zero <- which(!ggs)[1]
                                if (is.na(first_zero)) first_zero <- length(ggs) + 1
                                last_one <- max(which(ggs[1:(first_zero - 1)]))
                                paste0(gg[c(1, last_one)], collapse = " ")
                              }) %>% unlist()
# join it with party dictionairy
un_authors2 <- un_authors %>% left_join(y = posl_df %>% select(stripped = AUTHOR, period, ugr2 = ugr), by = c("stripped", "period"))

# repeat manual changes and join with text data and see if that he
all_pi$AUTHOR[all_pi$AUTHOR == "łukasz Zbonikowski"] <- "Łukasz Zbonikowski"

all_pi %>%
  left_join(un_authors2 %>% select(AUTHOR, period, ugr), by = c("AUTHOR", "period")) %>%
  left_join(un_authors2 %>% select(AUTHOR, stripped, period, ugr2) %>% unique(), by = c("AUTHOR", "period")) %>%
  group_by(period) %>% 
  summarise(count_original = n_distinct(AUTHOR), count_clean = n_distinct(stripped), count_int = n(), 
            perc_without_original = sum(is.na(ugr)) / count_int, perc_without_clean = sum(is.na(ugr2)) / count_int) %>% 
  kable() %>% 
  kable_styling()

period	count_original	count_clean	count_int	perc_without_original	perc_without_clean
1997-2001	559	339	7444	0.1575766	0.0569586
2001-2005	687	435	10906	0.1444159	0.0790391
2005-2007	685	429	9752	0.2335931	0.0796760

After clean up, instead of 1 in 5 interpellations without an author now I have only 1 in 12 missing.

all_pi <- all_pi %>% 
  left_join(un_authors2 %>% select(AUTHOR, AUTHOR_CLEAN = stripped, period, party = ugr2) %>% unique(), by = c("AUTHOR", "period"))

Clean stemmed data

The data was stem outsied the script - load it

# I've tagged already
pi_tagged_files <- paste0("tagged_", pi_files)
all_pi_tagged <- do.call(rbind, 
                         lapply(X = pi_tagged_files, 
                                FUN = function(i){
                                  mydb <- dbConnect(SQLite(), file.path("data", i))
                                  this_cont <- dbGetQuery(conn = mydb, statement = "select * from tagged")
                                  dbDisconnect(mydb)
                                  this_cont$period <- gsub("tagged_pi_", replacement = "", x = gsub(pattern = ".sqlite", replacement = "", x = i))
                                  this_cont
                                }))

head(all_pi_tagged) %>% 
  kable() %>% 
  kable_styling()

ID	ORG	TAGGED	period
1	Na	na	1997-2001
1	początku	początek	1997-2001
1	bieżącego	bieżący	1997-2001
1	roku	rok	1997-2001
1	zwrócił	zwrócić	1997-2001
1	em	być	1997-2001

The data is stripped from whitespaces and lowercased (unless it is a recognizable name). How many tags (stemmed entities) do I have?

n_distinct(all_pi_tagged$TAGGED)

## [1] 64840

Cleaning:

• remove words that correspond to dates
• remove polish stop words
• remove number (in several formats)
• remove dates (in several formats)
• remove words that are most likely in a greeting or in the signature part of interpellation

# cleaning
all_pi_tagged_clean <- all_pi_tagged %>% 
  # remove part with date
  anti_join(y = data.frame(TAGGED = c("dzień", "styczeń", "luty", "marzec", "kwiecień", "maj", "czerwiec", "lipiec", "sierpień", "wrzesień",
                                      "październik", "listopad", "grudzień", "rok")), by = "TAGGED") %>% 
  # remove stopwords
  anti_join(y = polish_stop_words_df("TAGGED"))

# remove numbers
all_pi_tagged_clean <- all_pi_tagged_clean[regexpr(pattern = "^\\d+$", text = all_pi_tagged_clean$TAGGED) < 0, ]

# %, +
all_pi_tagged_clean <- all_pi_tagged_clean[regexpr(pattern = "^\\W$", text = all_pi_tagged_clean$TAGGED) < 0, ]

# numbers with comma as decimal point
all_pi_tagged_clean <- all_pi_tagged_clean[regexpr(pattern = "^\\d+,\\d+$", text = all_pi_tagged_clean$TAGGED) < 0, ]

# numbers with dot as decimal point
all_pi_tagged_clean <- all_pi_tagged_clean[regexpr(pattern = "^\\d+\\.\\d+$", text = all_pi_tagged_clean$TAGGED) < 0, ]

# remove dates xx.xx.xxxx
all_pi_tagged_clean <- all_pi_tagged_clean[regexpr(pattern = "^\\d{1,2}\\.(0\\d{1}|\\d{1,2})\\.(\\d{2}|\\d{4})$", text = all_pi_tagged_clean$TAGGED) < 0, ]

# remove dates xx-xx-xxxx
all_pi_tagged_clean <- all_pi_tagged_clean[regexpr(pattern = "^\\d{1,2}-(0\\d{1}|\\d{1,2})-(\\d{2}|\\d{4})$", text = all_pi_tagged_clean$TAGGED) < 0, ]

# remove words like 'szanowny', 'minister', 'premier' because they come from a greeting at the beginnig of the interpellation or at the end ("poseł", "poważanie")
all_pi_tagged_clean <- all_pi_tagged_clean %>% filter(!TAGGED %in% c("szanowny", "minister", "premier", "poseł", "poważanie"))

How many tags are in the end?

un_tagged <- sort(unique(all_pi_tagged_clean$TAGGED))
length(un_tagged)

## [1] 56516

In order to incorporate Text Mining algorithms I’ll create documents from processed stemmed data.

all_pi_tagged <- all_pi_tagged_clean %>% 
  group_by(period, ID) %>% 
  summarise(CONTENT = paste0(TAGGED, collapse = " "))

head(all_pi_tagged, 2) %>% 
  kable() %>% 
  kable_styling()

period	ID	CONTENT
1997-2001	1	początek bieżący zwrócić zdrowie sprawa przestrzegać przepis ustawa dotyczyć dopuszczalność przerywać ciąża wskazać niektóry województwo województwo kielecki obowiązywać ustawa kobieta chcieć skorzystać przysługiwać prawo mieć możliwość wynikać fakt ginekolog publiczny placówka opieka zdrowotny gremialnie odmawiać wykonać dopuszczalny prawnie zabieg spełnić zainteresowany kobieta przewidzieć przepis wymóg zaznaczyć przepis dawać określić prawo kobieta mieć obowiązek uprawnienie gwarantować odpowiedź zdrowie zobowiązać podjąć działanie zmierzać województwo kielecki doprowadzić prawidłowy funkcjonować ustawa zgodzić chyba móc województwo mieć placówka kobieta móc liczyć prawo uszanować niezrozumiały chóralny powoływać ginekolog kodeks etyka lekarski często ginekolog wykonywać zabieg aborcja prywatnie zacisze własny gabinet oczywiście odpowiedni opłata oceniać szczyt hipokryzja pomijać kodeks zawodowy móc sytuować obowiązujący prawo podstawowy problem brzmieć kwestia przysługiwać kobieta prawo rozwiązać zgodnie przepis ustawa dotąd nikt przepis zmienić wydać wejście życie nowy konstytucja orzeczenie trybunał konstytucyjny mieć moc stanowić mieć sytuacja obowiązujący prawo przypadek prawo papier chcieć spytać zatem raz działanie resort zdrowie podjąć podjąć przysługiwać kobieta prawo przestrzegać wreszcie sprawa zostać rozwiązać gwarantować kobieta minimum godność prosić konkretny informacja efekt dotychczasowy działanie podjąć województwo kielecki ostatni czas zgłaszać biuro poselski kobieta wskazywać prywatnie pieniądz zabieg móc mieć wykonać jednocześnie lekarz wojewódzki stan wskazać placówka móc mieć wykonać zabieg darmo uważać sytuacja wyjątkowo bulwersujący Władysław Adamski Warszawa
1997-2001	2	trwać protest anestezjolog przeciwny polityka rząd RP dyskryminować lekarz okręg gliwicki głodować anestezjolog szpital główny postulat wyposażyć szpital sprzęt anestezjologiczny często stan użyć zagrażać życie zdrowie pacjent wzrost wynagrodzenie lekarz anestezjolog maksymalny stawka dany grupa zaszeregowanie plus premia kwota niski średni krajowy lekarz wojewódzki A Sośnierz rozmowa głodować lekarz wygospodarować pieniądz rozesłać dyrektor szpital niestety podwyżka rezultat rząd złoty zdrowie lekarz podległy pacjent zagrozić sprawa oczywisty wymagać natychmiastowy realizacja zwracać Jan Olszewski Warszawa

Basic information

Basic information regarding data

all_pi %>% group_by(period) %>% 
  summarise(count = n(), autohrs = n_distinct(AUTHOR_CLEAN), av_len = mean(len), sd_len = sd(len), min_len = min(len), max_len = max(len)) %>% 
  kable() %>% 
  kable_styling()

period	count	autohrs	av_len	sd_len	min_len	max_len
1997-2001	7444	339	2002.257	1371.682	260	20858
2001-2005	10906	435	2377.523	2084.135	65	53942
2005-2007	9724	429	2274.355	1594.731	82	20618

For each period there is between 7.4k and 10.9k documents of average length of 2k characters. See how the interpolation number by date changed.

all_pi %>% 
  group_by(date) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(x = date, y = count)) +
  geom_col() + 
  geom_smooth() +
  theme_minimal()

One can clearly see that the number of interpollation increases in time with some seasonal variations. Lets interpollation number by period but counting from the start of the term.

all_pi %>% 
  left_join(y = period_bands, by = "period") %>% 
  mutate(days_by = as.numeric(date - start_date)) %>% 
  group_by(days_by, period) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(x = days_by, y = count, group = period)) +
  geom_point(alpha = .1) + 
  geom_smooth() + 
  facet_wrap(~period, scales = "free") +
  theme_minimal()

Which words appear the most?

summ_data <- all_pi_tagged_clean %>% 
  left_join(y = all_pi %>% select(ID, period, date)) %>% 
  group_by(TAGGED, date) %>% 
  summarise(count = n(), n_ids = n_distinct(ID)) %>% 
  ungroup() %>% 
  arrange(TAGGED, date)

First look at the count statistics. It measures how many times in a single day the word occurred.

max_tagged <- summ_data %>% group_by(TAGGED) %>% summarise(count_mx = max(count)) %>% top_n(n = 10, wt = count_mx) %>% .$TAGGED
show_ridge(summ_data = summ_data, tt = max_tagged) +
  theme_minimal()

There are visible spikes with words like zarząd, spółka, komisaryczny, FSO, daewoo in similar time. This most probably corresponds to the issue of bankruptcy of Daewoo with was main partner of FSO (link1 and link2).

Another way to look is for number of documents in which terms occured.

max_ids <- summ_data %>% group_by(TAGGED) %>% summarise(ids_mx = max(n_ids)) %>% top_n(n = 10, wt = ids_mx) %>% .$TAGGED
show_ridge(summ_data = summ_data, tt = max_ids) +
  theme_minimal()

There is little to none information value from those words. All of them corresponds to the fact of simply asking the question (pytanie, sprawa etc.). Even word Warszawa might not realte to the interpollation subject but rather comes from the interpollation signature.

For what words the frequency fluctuate mostly?

kk <- summ_data %>% group_by(TAGGED) %>% 
  summarise(n_days = as.numeric(max(date) - min(date)), mn = sd(count)) %>% 
  filter(n_days > 0)

show_ridge(summ_data = summ_data, tt = kk %>% filter(n_days > 100) %>% top_n(n = 15, mn) %>% .$TAGGED) +
  theme_minimal()

Other than words mentioned earlier

Clusterization

For each period I’ll perform these steps:

• create dtm object
• based on dtm object create tf_mat object
• based on tf_mat object create cdist object
• based on dtm object create p_words object

dtms <- lapply(X = period_bands$period, FUN = function(x) dtm_from_text(all_pi_tagged %>% filter(period == x), "CONTENT", "ID"))
names(dtms) <- period_bands$period

# develop the matrix of term counts to get the IDF vector
tf_mats <- lapply(X = period_bands$period, FUN = function(x) TermDocFreq(dtms[[x]]))
names(tf_mats) <- period_bands$period

# calculate distances
cdists <- lapply(X = period_bands$period, FUN = function(x) calc_cdist(dtms[[x]], tf_mats[[x]]))
names(cdists) <- period_bands$period

# use the probability difference method
p_words <- lapply(X = period_bands$period, FUN = function(x) colSums(dtms[[x]]) / sum(dtms[[x]]))
names(p_words) <- period_bands$period

Each of the tabset below consists analysis for each term. Some of the key insights:

• set of the most frequent words in all of terms is similar: Polska, polski, ustawa, mieć, móc, sprawa, prawo, praca
• in 1997-2001 clusterization analysis revealed such topics as healthcare (zdrowie, chory, zdrowotny, kasa, opieka), state treasury (spółka, SA, skarb, skarb_państwo, państwo), education (szkoła, nauczyciel, dziecko, gmina, edukacja), social security (ubezpieczenie, składka, społeczny, ubezpieczenie_społeczny, ZUS), taxes (podatek, podatkowy, podatnik, skarbowy, artykuł), housing (mieszkaniowy, lokal, mieszkanie, spółdzielnia, budynek), job market (praca, urząd, urząd_praca, bezrobocie, środek) and social benefits (pomoc, społeczny, pomoc_społeczny, rodzina, dom)
• the topics in 2001-2005 were partially similar: treasury (spółka, SA, skarb, skarb_państwo, państwo), education (szkoła, dziecko, nauczyciel, rodzina, uczeń), job market and social insurance (praca, ubezpieczenie, społeczny, ZUS, emerytalny), disability (niepełnosprawny, osoba, osoba_niepełnosprawny, praca, rehabilitacja), infrastructure (droga, autostrada, budowa, kolejowy, PKP), healthcare (zdrowie, szpital, zdrowotny, pacjent, chory) and taxes (podatek, VAT, usługa, towar, stawka)
• in 2005-2007 clusters corresponded to general topics such as Polands’ development (polski, spółka, Polska, sprawa, rozwój), education (szkoła, nauczyciel, uczeń, egzamin, uczelnia), real estate (podatek, nieruchomość, ustawa, podatkowy, mieszkaniowy), social benefits (praca, osoba, społeczny, dziecko, świadczenie), healthcare (zdrowie, szpital, lekarz, lek, pacjent), infrastructure (droga, budowa, autostrada, odcinek, krajowy) and very specific as HCV (HCV, wątroba, typ, leczenie, zapalenie), donating blood (krew, honorowy, krwiodawca, darowizna, podatek) or building supervision (budowlany, nadzór, nadzór_budowlany, inspektorat, powiatowy)

1997-2001

Top words (term frequency)

nn <- "1997-2001"
show_top(tf_mats[[nn]], title = "Top words")

Top words (doc frequency)

show_top(tf_mats[[nn]], wt = "doc_freq", "Top words (doc frequency)")

Ward’s clusterization (9 clusters)

n_clust <- 9
show_hierarch(cdists[[nn]], n_clust = n_clust)

Top words in each cluster

clustering <- calc_clust(cdists[[nn]], n = n_clust)
cluster_words <- lapply(X = unique(clustering), 
                        FUN = function(x){
                          rows <- dtms[[1]][ clustering == x , ]
                          rows <- rows[ , colSums(rows) > 0 ]
                          colSums(rows) / sum(rows) - p_words[[1]][ colnames(rows) ]
                        })

cluster_summary <- data.frame(cluster = unique(clustering),
                              size = as.numeric(table(clustering)),
                              top_words = sapply(cluster_words, function(d){
                                paste(
                                  names(d)[ order(d, decreasing = TRUE) ][ 1:5 ], 
                                  collapse = ", ")
                              }),
                              stringsAsFactors = FALSE)
cluster_summary %>% 
  kable() %>% 
  kable_styling()

cluster	size	top_words
1	4802	polski, Polska, sprawa, europejski, gospodarka
2	603	zdrowie, chory, zdrowotny, kasa, opieka
3	393	spółka, SA, skarb, skarb_państwo, państwo
4	645	szkoła, nauczyciel, dziecko, gmina, edukacja
5	267	ubezpieczenie, składka, społeczny, ubezpieczenie_społeczny, ZUS
6	208	podatek, podatkowy, podatnik, skarbowy, artykuł
7	158	mieszkaniowy, lokal, mieszkanie, spółdzielnia, budynek
8	238	praca, urząd, urząd_praca, bezrobocie, środek
9	130	pomoc, społeczny, pomoc_społeczny, rodzina, dom

Wordcloud for 2nd cluster

plot_word_cloud(cluster_words, specific_cluster = 2)

2001-2005

Top words (term frequency)

nn <- "2001-2005"
show_top(tf_mats[[nn]], title = "Top words")

Top words (doc frequency)

show_top(tf_mats[[nn]], wt = "doc_freq", "Top words (doc frequency)")

Ward’s clusterization (8 clusters)

n_clust <- 8
show_hierarch(cdists[[nn]], n_clust = n_clust)

Top words in each cluster

clustering <- calc_clust(cdists[[nn]], n = n_clust)
cluster_words <- lapply(X = unique(clustering), 
                        FUN = function(x){
                          rows <- dtms[[nn]][ clustering == x , ]
                          rows <- rows[ , colSums(rows) > 0 ]
                          colSums(rows) / sum(rows) - p_words[[nn]][ colnames(rows) ]
                        })

cluster_summary <- data.frame(cluster = unique(clustering),
                              size = as.numeric(table(clustering)),
                              top_words = sapply(cluster_words, function(d){
                                paste(
                                  names(d)[ order(d, decreasing = TRUE) ][ 1:5 ], 
                                  collapse = ", ")
                              }),
                              stringsAsFactors = FALSE)
cluster_summary %>% 
  kable() %>% 
  kable_styling()

cluster	size	top_words
1	6618	polski, sprawa, sąd, Polska, prawo
2	1081	spółka, SA, skarb, skarb_państwo, państwo
3	907	szkoła, dziecko, nauczyciel, rodzina, uczeń
4	416	praca, ubezpieczenie, społeczny, ZUS, emerytalny
5	130	niepełnosprawny, osoba, osoba_niepełnosprawny, praca, rehabilitacja
6	431	droga, autostrada, budowa, kolejowy, PKP
7	1118	zdrowie, szpital, zdrowotny, pacjent, chory
8	205	podatek, VAT, usługa, towar, stawka

Wordcloud for 3rd cluster

plot_word_cloud(cluster_words, specific_cluster = 3)

2005-2007

Top words (term frequency)

nn <- "2005-2007"
show_top(tf_mats[[nn]], title = "Top words")

Top words (doc frequency)

show_top(tf_mats[[nn]], wt = "doc_freq", "Top words  (doc frequency)")

Ward’s clusterization (9 clusters)

n_clust <- 9
show_hierarch(cdists[[nn]], n_clust = n_clust)

Top words in each cluster

clustering <- calc_clust(cdists[[nn]], n = n_clust)
cluster_words <- lapply(X = unique(clustering), 
                        FUN = function(x){
                          rows <- dtms[[nn]][ clustering == x , ]
                          rows <- rows[ , colSums(rows) >  0 ]
                          colSums(rows) / sum(rows) - p_words[[nn]][ colnames(rows) ]
                        })

cluster_summary <- data.frame(cluster = unique(clustering),
                              size = as.numeric(table(clustering)),
                              top_words = sapply(cluster_words, function(d){
                                paste(
                                  names(d)[ order(d, decreasing = TRUE) ][ 1:5 ], 
                                  collapse = ", ")
                              }),
                              stringsAsFactors = FALSE)
cluster_summary %>% 
  kable() %>% 
  kable_styling()

cluster	size	top_words
1	5713	polski, spółka, Polska, sprawa, rozwój
2	563	szkoła, nauczyciel, uczeń, egzamin, uczelnia
3	1180	podatek, nieruchomość, ustawa, podatkowy, mieszkaniowy
4	35	HCV, wątroba, typ, leczenie, zapalenie
5	1088	praca, osoba, społeczny, dziecko, świadczenie
6	690	zdrowie, szpital, lekarz, lek, pacjent
7	357	droga, budowa, autostrada, odcinek, krajowy
8	51	krew, honorowy, krwiodawca, darowizna, podatek
9	47	budowlany, nadzór, nadzór_budowlany, inspektorat, powiatowy

Wordcloud for 7th cluster

plot_word_cloud(cluster_words, specific_cluster = 7)

Topic modelling

Initial clustering gave an idea regarding probable topics in each term. Another way to tackle this problem is to use topic modelling.

To start, for each term, I’ll create list of top 6 topic using Gibbs method (although the analyed number of cluster for each period was higher this is time consuming operation)

lda_outs <- lapply(X = period_bands$period,
                   FUN = function(x) 
                     LDA(dtms[[x]],
                         k = 6,
                         method = "Gibbs",
                         control = list(seed = 306068)))
names(lda_outs) <- period_bands$period

Results of topic modelling for the 1997-2001 term

plot_topics_from_lda_out(lda_out = lda_outs[[1]]) + labs(title = "1997-2001 period")

Results of topic modelling for the 2001-2005 term

plot_topics_from_lda_out(lda_out = lda_outs[[2]]) + labs(title = "2001-2005 period")

Results of topic modelling for the 2005-2007 term

plot_topics_from_lda_out(lda_out = lda_outs[[3]]) + labs(title = "2005-2007 period")

I’ll try to show the results differently

show_topics_diff_periods(lda_outs, sa = c(.5, 1), st = c(5, 8.1))

In 1997-2001 period 6 major topics were:

• state treasury (polski, skarb, przedsiębiorstwo)
• job market (praca, pracownik)
• law (ustawa, prawo)
• regional state issues (środek, województwo, gminna)
• healthcare (zdrowie, opieka, chory)

In 2001-2005 period major topics were :

• foreign policy (europejski, kraj)
• job market (praca, pracownik)
• economy (spółka, firma, zakład)
• court (sprawa, sąd)
• regional state issues (środek, województwo, gminna)
• law (ustawa, prawo)

In 2005-2007 term major topic were:

• infrastructure (droga, budowa)
• domestic economy (polski, firma, spółka)
• social issues (praca, społeczny, dziecko)
• general issueas (sprawa, służba)
• law (ustawa, prawo)
• healthcare (zdrowie, szpital)

To quantify how the topics change more gradually I’ll split the dataset into overlapping periods. Graph below shows such split (lines present number of interpellation in each period).

new_periods <- do.call(rbind,
                       lapply(X = period_bands$period,
                              FUN = function(x){
                                tt <- period_bands %>% filter(period == x)
                                n_days <- 180
                                ta <- seq(from = tt$start_date, by = n_days, 
                                          length.out = ceiling((tt$end_date - tt$start_date) / n_days))
                                ta[length(ta)] <- tt$end_date
                                overlap <- 1 # number of periods to overlap
                                cbind(data.frame(period = x),
                                      data.frame(st = ta[1:(length(ta) - (1 + overlap))], et = ta[(2 + overlap):length(ta)]))
                              }))
new_periods <- new_periods %>% 
  mutate(p_label = paste0(period, " (", format(st, "%Y-%m"), " - ", format(et, "%Y-%m"), ")"))

do.call(rbind,
        lapply(X = 1:nrow(new_periods),
               FUN = function(x){
                 oo <- new_periods[x, ]
                 res <- all_pi %>% filter(date <= oo$et & date >= oo$st) %>% 
                   summarise(count = n())
                 cbind(oo, res)
               })) %>% 
  ggplot(mapping = aes(x = et, xend = st, y = count, yend = count)) +
  geom_segment(linewidth = 2) +
  scale_y_continuous(limits = function(x) c(0, max(x))) + 
  theme_minimal()

For each split:

• create dtm

dtms_split <- lapply(X = new_periods$p_label, 
                     FUN = function(x){
                       oo <- new_periods %>% filter(p_label == x)
                       all_pi %>% 
                         filter(date <= oo$et & date >= oo$st) %>% 
                         select(period, ID) %>% 
                         left_join(y = all_pi_tagged, by = c("ID", "period")) %>% 
                         dtm_from_text("CONTENT", "ID")
                     })
names(dtms_split) <- new_periods$p_label

• perform topic modelling

lda_outs_split <- lapply(X = new_periods$p_label,
                   FUN = function(x) 
                     LDA(dtms_split[[x]],
                         k = 6,
                         method = "Gibbs",
                         control = list(seed = 306068)))
names(lda_outs_split) <- new_periods$p_label

Chart below presents the results.

show_topics_diff_periods(lda_outs_split, sa = c(.5, 1), st = c(4, 7))

As one can see some topics are prevalent, no matter what granulation: healthcare, regional issues, job market and law. Also some topics were only visible in short periods like agriculture or education.

Another way to look at change of topics is to look at a single member. Here are top 5 members which were present in all periods and authored most interpellations.

all_pi %>% 
  filter(!is.na(party)) %>% 
  group_by(AUTHOR_CLEAN) %>% 
  summarise(dist_period = n_distinct(period), n_int = n()) %>% 
  filter(dist_period == 3) %>% 
  top_n(n = 5, wt = n_int) %>% arrange(desc(n_int)) %>% 
  kable() %>% 
  kable_styling()

AUTHOR_CLEAN	dist_period	n_int
Anna Sobecka	3	786
Stanisław Stec	3	555
Janusz Dobrosz	3	248
Grzegorz Kurczuk	3	240
Jerzy Budnik	3	195

For further analysis I choose Anna Sobecka since number of her interpellations (nearly 800) might be enough to perform topic modelling.

dtms_split_top_member <- lapply(X = period_bands$period, 
                                FUN = function(x){
                                  all_pi %>% 
                                    filter(period == x & AUTHOR_CLEAN == "Anna Sobecka") %>% 
                                    select(period, ID) %>% 
                                    left_join(y = all_pi_tagged, by = c("ID", "period")) %>% 
                                    dtm_from_text("CONTENT", "ID")
                                })
names(dtms_split_top_member) <- period_bands$period
lda_outs_split_top_member <- lapply(X = period_bands$period,
                                    FUN = function(x) 
                                      LDA(dtms_split_top_member[[x]],
                                          k = 6,
                                          method = "Gibbs",
                                          control = list(seed = 306068)))
names(lda_outs_split_top_member) <- period_bands$period

As the chart suggests the topic covered by this member vary over time: from railway, new doctor training through healthcare and law up to building roads.

show_topics_diff_periods(lda_outs_split_top_member, sa = c(.5, 1), st = c(5, 8.1))

Evaluation

The text mining project utilizing topic modeling to analyze interpellations of Polish parliament members shows promising potential for uncovering significant patterns and themes within the political discourse. However, further refinement in the data preparation and extending time horizon could lead to more robust and insightful results, potentially enhancing the project’s overall effectiveness in capturing the nuances of parliamentary exchanges and contributing to a deeper understanding of political dynamics within Poland.

Summary

1. Was the purpose achieved?

The purpose of this project was achieved - I’ve successfully implement topic modelling into interpellations of Polish parliament members. Additionally I’ve presented some new ways of presenting the findings.

2. Were the assumptions examined?

First assumption: There are specific topics emerge as dominant themes across the interpellation corpus, indicating their prevalence and importance within the legislative agenda.

The analyse support that claim. Additionally topics found in data are consistent with common sense regarding this data.

Second assumption: Importance of topics may vary across different parliamentary sessions, political factions, or policy domains, reflecting shifts in public attention, government priorities, or societal concerns over time.

I believe I’ve also proved this assumption. The topics vary not only between periods but also intra-period suggesting ongoing fluctuaction of politicians’ interests.

3. Conclusion

Topic modelling based on interpellations from Members of Polish Parliament is not only possible but also unveils pattern in political corpus.The result could be improved by improving text porcessing process or/and by adding more periods for analysis. Tools like clustering or LDA are perfect for performing such analysis.

Appendix with source code and data set

All the code is available at my Github repository. Preprocessed files are available on Google Drive. In order to run this file one needs to:

• clone the repository
• copy data from Google Drive to data directory inside the project directory